| | import os |
| | import torch |
| | import torch.nn as nn |
| | from mono.utils.comm import main_process |
| | import copy |
| | import inspect |
| | import logging |
| | import glob |
| |
|
| |
|
| | def load_ckpt(load_path, model, optimizer=None, scheduler=None, strict_match=True, loss_scaler=None): |
| | """ |
| | Load the check point for resuming training or finetuning. |
| | """ |
| | logger = logging.getLogger() |
| | if os.path.isfile(load_path): |
| | if main_process(): |
| | logger.info(f"Loading weight '{load_path}'") |
| | checkpoint = torch.load(load_path, map_location="cpu") |
| | ckpt_state_dict = checkpoint['model_state_dict'] |
| | model.module.load_state_dict(ckpt_state_dict, strict=strict_match) |
| |
|
| | if optimizer is not None: |
| | optimizer.load_state_dict(checkpoint['optimizer']) |
| | if scheduler is not None: |
| | scheduler.load_state_dict(checkpoint['scheduler']) |
| | if loss_scaler is not None and 'scaler' in checkpoint: |
| | scheduler.load_state_dict(checkpoint['scaler']) |
| | del ckpt_state_dict |
| | del checkpoint |
| | if main_process(): |
| | logger.info(f"Successfully loaded weight: '{load_path}'") |
| | if scheduler is not None and optimizer is not None: |
| | logger.info(f"Resume training from: '{load_path}'") |
| | else: |
| | if main_process(): |
| | raise RuntimeError(f"No weight found at '{load_path}'") |
| | return model, optimizer, scheduler, loss_scaler |
| |
|
| |
|
| | def save_ckpt(cfg, model, optimizer, scheduler, curr_iter=0, curr_epoch=None, loss_scaler=None): |
| | """ |
| | Save the model, optimizer, lr scheduler. |
| | """ |
| | logger = logging.getLogger() |
| |
|
| | if 'IterBasedRunner' in cfg.runner.type: |
| | max_iters = cfg.runner.max_iters |
| | elif 'EpochBasedRunner' in cfg.runner.type: |
| | max_iters = cfg.runner.max_epochs |
| | else: |
| | raise TypeError(f'{cfg.runner.type} is not supported') |
| |
|
| | ckpt = dict( |
| | model_state_dict=model.module.state_dict(), |
| | optimizer=optimizer.state_dict(), |
| | max_iter=cfg.runner.max_iters if 'max_iters' in cfg.runner \ |
| | else cfg.runner.max_epochs, |
| | scheduler=scheduler.state_dict(), |
| | ) |
| |
|
| | if loss_scaler is not None: |
| | ckpt.update(dict(scaler=loss_scaler.state_dict())) |
| | |
| | ckpt_dir = os.path.join(cfg.work_dir, 'ckpt') |
| | os.makedirs(ckpt_dir, exist_ok=True) |
| |
|
| | save_name = os.path.join(ckpt_dir, 'step%08d.pth' %curr_iter) |
| | saved_ckpts = glob.glob(ckpt_dir + '/step*.pth') |
| | torch.save(ckpt, save_name) |
| |
|
| | |
| | if len(saved_ckpts) > 20: |
| | saved_ckpts.sort() |
| | os.remove(saved_ckpts.pop(0)) |
| | |
| | logger.info(f'Save model: {save_name}') |
| |
|
